from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
				from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


A						=			logging.get_logger(__name__)

A						=			{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'deberta-v2'
		def __init__(       self     :     Any     ,  snake_case__     :     Dict=1_2_8_1_0_0     ,  snake_case__     :     str=1_5_3_6     ,  snake_case__     :     Any=2_4     ,  snake_case__     :     int=2_4     ,  snake_case__     :     str=6_1_4_4     ,  snake_case__     :     str="gelu"     ,  snake_case__     :     int=0.1     ,  snake_case__     :     int=0.1     ,  snake_case__     :     List[Any]=5_1_2     ,  snake_case__     :     Optional[Any]=0     ,  snake_case__     :     Dict=0.02     ,  snake_case__     :     Union[str, Any]=1e-7     ,  snake_case__     :     List[str]=False     ,  snake_case__     :     Optional[Any]=-1     ,  snake_case__     :     Union[str, Any]=0     ,  snake_case__     :     Any=True     ,  snake_case__     :     Any=None     ,  snake_case__     :     Union[str, Any]=0     ,  snake_case__     :     Dict="gelu"     ,  **snake_case__     :     Optional[Any]     ,  )      ->	Optional[Any]:
				super().__init__(**snake_case__		)

				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_hidden_layers
				_lowerCamelCase				   =  num_attention_heads
				_lowerCamelCase				   =  intermediate_size
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  hidden_dropout_prob
				_lowerCamelCase				   =  attention_probs_dropout_prob
				_lowerCamelCase				   =  max_position_embeddings
				_lowerCamelCase				   =  type_vocab_size
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  relative_attention
				_lowerCamelCase				   =  max_relative_positions
				_lowerCamelCase				   =  pad_token_id
				_lowerCamelCase				   =  position_biased_input

				# Backwards compatibility
				if type(snake_case__		) == str:
						_lowerCamelCase				   =  [x.strip() for x in pos_att_type.lower().split('|'		)]

				_lowerCamelCase				   =  pos_att_type
				_lowerCamelCase				   =  vocab_size
				_lowerCamelCase				   =  layer_norm_eps

				_lowerCamelCase				   =  kwargs.get('pooler_hidden_size'     ,  snake_case__		)
				_lowerCamelCase				   =  pooler_dropout
				_lowerCamelCase				   =  pooler_hidden_act





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@property
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
						_lowerCamelCase				   =  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						_lowerCamelCase				   =  {0: 'batch', 1: 'sequence'}
				if self._config.type_vocab_size > 0:
						return OrderedDict(
						    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]		)
				else:
						return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]		)
		@property
		def   _snake_case       (       self     :     int		)      ->	int:
				return 1_2



		def   _snake_case       (       self     :     Any     ,  snake_case__     :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]     ,  snake_case__     :     int = -1     ,  snake_case__     :     int = -1     ,  snake_case__     :     int = -1     ,  snake_case__     :     bool = False     ,  snake_case__     :     Optional["TensorType"] = None     ,  snake_case__     :     int = 3     ,  snake_case__     :     int = 4_0     ,  snake_case__     :     int = 4_0     ,  snake_case__     :     "PreTrainedTokenizerBase" = None     ,  )      ->	Mapping[str, Any]:
				_lowerCamelCase				   =  super().generate_dummy_inputs(preprocessor=snake_case__     ,  framework=snake_case__		)
				if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
						del dummy_inputs["token_type_ids"]
				return dummy_inputs
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()